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Open-source pipeline for multi-class segmentation of the spinal cord with deep learning.

Authors :
Paugam, François
Lefeuvre, Jennifer
Perone, Christian S.
Gros, Charley
Reich, Daniel S.
Sati, Pascal
Cohen-Adad, Julien
Source :
Magnetic Resonance Imaging (0730725X). Dec2019, Vol. 64, p21-27. 7p.
Publication Year :
2019

Abstract

This paper presents an open-source pipeline to train neural networks to segment structures of interest from MRI data. The pipeline is tailored towards homogeneous datasets and requires relatively low amounts of manual segmentations (few dozen, or less depending on the homogeneity of the dataset). Two use-case scenarios for segmenting the spinal cord white and grey matter are presented: one in marmosets with variable numbers of lesions, and the other in the publicly available human grey matter segmentation challenge [ 1 ]. The pipeline is freely available at: https://github.com/neuropoly/multiclass-segmentation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0730725X
Volume :
64
Database :
Academic Search Index
Journal :
Magnetic Resonance Imaging (0730725X)
Publication Type :
Academic Journal
Accession number :
139748423
Full Text :
https://doi.org/10.1016/j.mri.2019.04.009